AI can assist with data and analysis, but only researchers can define meaning by asking the right questions, interpreting results, and grounding insights in ethical, social, and human contexts. To ensure this, researchers must use AI as a tool—not a truth source—maintain critical thinking, uphold peer review, and stay transparent about AI’s role in their work.

We need to put AI into a system of human-led epistemology so that researchers may still define knowledge. In this system, algorithms are used to come up with hypotheses, not to decide what is true. One new idea is to make AI systems that need human validation checkpoints. Only findings that have been peer-reviewed or explained by professionals in the field are formally added to the scientific canon. Also, AI that is open about its logic and presents it in a way that people can comprehend might encourage teamwork instead than replacing people. This keeps the researcher in charge of what things mean, making sure that science stays a human activity that AI can help with but not control.

I totally get why AI's changing how we learn and create knowledge, it's super fast! But it can't think deeply or ask big questions like we do. As researchers, we have got to double check AI's work to keep science honest. Teaching economics means showing students how to spot when algorithms get it wrong, so they use AI smartly.

To ensure researchers define knowledge, AI must be used as a tool for analysis, not authority—e.g., a researcher interprets AI Summarised interviews through their theoretical lens. Embedding human judgment preserves context, ethics, and critical insight that AI alone cannot provide.

I suspect the short answer to your question is: you cannot, it's already too late. There is much more powerful AI on the way! Do not forget, for example, that the world chess champion is a computer program! And think of the military applications being quietly worked upon. James

The Post Science Paradigm of Scientific Discovery in the Era of Artificial Intelligence: Modelling the Collapse of Ideation Costs, Epistemic Inversion, and the End of Knowledge Scarcity

"This paper develops a theoretical and formal response to the collapse in the marginal cost of ideation caused by artificial intelligence (AI). In challenging the foundational assumption of knowledge scarcity, the paper argues that the key economic constraint is no longer the generation of ideas, but the alignment of ideation with the recursive structure of human needs. Building on previous work, we further develop Experiential Matrix Theory (EMT), a framework that models innovation as a recursive optimisation process in which alignment, rather than ideation, becomes the binding constraint. Accordingly, we formalise core mechanisms of EMT and apply it to the dynamics of ideation collapse and institutional realignment under AI. Using a series of defensible economic models, we show that in this post-scarcity paradigm, the creation of economic and social value increasingly accrues to roles that guide, interpret, and socially embed ideation, rather than to those that merely generate new ideas. The paper theorises a transition from a knowledge economy to an alignment economy, and derives policy implications for labor hierarchies, subsidy structures, and institutional design. The university, in this context, must invert its function from knowledge transmission to epistemic alignment. The paper concludes by reframing growth not as a function of knowledge accumulation, but of how well society aligns its expanding cognitive capacity with the frontier of experiential human value. This redefinition of the innovation constraint implies a transformation of growth theory, policy design, and institutional purpose in the AI era."

https://www.researchgate.net/publication/393539189_The_Post_Science_Paradigm_of_Scientific_Discovery_in_the_Era_of_Artificial_Intelligence_Modelling_the_Collapse_of_Ideation_Costs_Epistemic_Inversion_and_the_End_of_Knowledge_Scarcity